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- import scipy.sparse as sparse
- import numpy as np
- __all__ = ['contingency_table']
- def contingency_table(
- im_true, im_test, *, ignore_labels=None, normalize=False, sparse_type="matrix"
- ):
- """
- Return the contingency table for all regions in matched segmentations.
- Parameters
- ----------
- im_true : ndarray of int
- Ground-truth label image, same shape as im_test.
- im_test : ndarray of int
- Test image.
- ignore_labels : sequence of int, optional
- Labels to ignore. Any part of the true image labeled with any of these
- values will not be counted in the score.
- normalize : bool
- Determines if the contingency table is normalized by pixel count.
- sparse_type : {"matrix", "array"}, optional
- The return type of `cont`, either `scipy.sparse.csr_array` or
- `scipy.sparse.csr_matrix` (default).
- Returns
- -------
- cont : scipy.sparse.csr_matrix or scipy.sparse.csr_array
- A contingency table. `cont[i, j]` will equal the number of voxels
- labeled `i` in `im_true` and `j` in `im_test`. Depending on `sparse_type`,
- this can be returned as a `scipy.sparse.csr_array`.
- """
- if ignore_labels is None:
- ignore_labels = []
- im_test_r = im_test.reshape(-1)
- im_true_r = im_true.reshape(-1)
- data = np.isin(im_true_r, ignore_labels, invert=True).astype(float)
- if normalize:
- data /= np.count_nonzero(data)
- cont = sparse.csr_array((data, (im_true_r, im_test_r)))
- if sparse_type == "matrix":
- cont = sparse.csr_matrix(cont)
- elif sparse_type != "array":
- msg = f"`sparse_type` must be 'array' or 'matrix', got {sparse_type}"
- raise ValueError(msg)
- return cont
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